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DeMatteis, JM, Young, LJ, Dahlhamer, J, Langley, RE, Murphy, J, Olson, K and Sharma, S (2020)

Falsification in Surveys

Tech Report prepared for AAPOR Council and the Executive Committee of the American Statistical Association by the members of the Task Force on Data Falsification.

ISSN/ISBN: Not available at this time. DOI: Not available at this time.

Abstract: INTRODUCTION:Data fabrication and falsification pose serious threats to the credibility of survey research. Falsified or fabricated data yield biased estimates, affect the precision of estimates, and impact multivariate relationships. The Office of Research Integrity (ORI) (ORI, 2002, p. 2) declared falsification as a form of scientific misconduct. In an era of tight budgets and increasing challenges for surveys, resources still need to be allocated to prevent, detect, and mitigate data falsification and fabrication. In considering the aspects covered by this report, it is also important to be clear about aspects not covered by the report. While data falsification and fabrication are also concerns in other fields, such as clinical trials, the focus of this report is on data falsification and fabrication in surveys. The types of data falsification, methods for preventing or detecting it, and the impact on analyses will be discussed for all facets of a survey, from start to finish. This report does not cover “task simplification” methods respondents use, such as stylistic response or satisficing (Blasius and Thiessen, 2015); nor does it cover outright respondent fraud, such as a single individual completing a web survey multiple times in order to collect incentives. While aspects of detecting these methods and their impacts on analyses are similar to those for falsification or fabrication by employees of the survey organization conducting the survey, the respondent’s impacts on survey data quality are not explicitly covered in this report nor does it cover unintentional errors that affect data quality, such as errors in data entry or coding.

@techReport{, AUTHOR = {Jill M. DeMatteis and Linda J. Young and James Dahlhamer and Ronald E. Langley and Joe Murphy and Kristen Olson and Sharan Sharma}, TITLE = {Falsification in Surveys}, INSTITUTION = {AAPOR Council and the Executive Committee of the American Statistical Association}, YEAR = {2020}, URL = {}, }

Reference Type: Technical Report

Subject Area(s): Accounting, Computer Science